AWGN-goodness is enough: capacity-achieving lattice codes based on dithered probabilistic shaping
File(s)1707.06688.pdf (495.02 KB)
Accepted version
OA Location
Author(s)
Campello, Antonio
Dadush, Daniel
Ling, C
Type
Journal Article
Abstract
In this paper we show that any sequence of infinite lattice constellations which is good for the unconstrained Gaussian channel can be shaped into a capacity-achieving sequence of codes for the power-constrained Gaussian channel under lattice decoding and non-uniform signalling. Unlike previous results in the literature, our scheme holds with no extra condition on the lattices (e.g. quantization-goodness or vanishing flatness factor), thus establishing a direct implication between AWGNgoodness, in the sense of Poltyrev, and capacity-achieving codes. Our analysis uses properties of the discrete Gaussian distribution in order to obtain precise bounds on the probability of error and achievable rates. In particular, we obtain a simple characterization of the finite-blocklength behavior of the scheme, showing that it approaches the optimal dispersion coefficient for high signalto- noise ratio. We further show that for low signal-to-noise ratio the discrete Gaussian over centered lattice constellations cannot achieve capacity, and thus a shift (or “dither”) is essentially necessary.
Date Issued
2019-03-01
Date Acceptance
2018-08-26
Citation
IEEE Transactions on Information Theory, 2019, 65 (3), pp.1961-1971
ISSN
0018-9448
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1961
End Page
1971
Journal / Book Title
IEEE Transactions on Information Theory
Volume
65
Issue
3
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Engineering
Information theory
channel coding
lattices
shaping
MODULATION
INEQUALITIES
CHANNEL
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Networking & Telecommunications
Publication Status
Published
Date Publish Online
2018-10-09